Deep Learning-enabled Detection and Classification of Bacterial Colonies using a Thin Film Transistor (TFT) Image Sensor

@article{Li2022DeepLD,
  title={Deep Learning-enabled Detection and Classification of Bacterial Colonies using a Thin Film Transistor (TFT) Image Sensor},
  author={Yuzhu Li and Tairan Liu and Hatice Ceylan Koydemir and Hongda Wang and Keelan O'Riordan and Bijie Bai and Yuta Haga and Junji Kobashi and Hitoshi Tanaka and Takaya Tamaru and Kazunori Yamaguchi and Aydogan Ozcan},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.03549}
}
Early detection and identification of pathogenic bacteria such as Escherichia coli ( E. coli ) is an essential task for public health. The conventional culture-based methods for bacterial colony detection usually take ≥24 hours to get the final read-out. Here, we demonstrate a bacterial colony-forming-unit (CFU) detection system exploiting a thin-film-transistor (TFT)-based image sensor array that saves ~12 hours compared to the Environmental Protection Agency (EPA)-approved methods. To… 
1 Citations

Figures from this paper

Stain-free, rapid, and quantitative viral plaque assay using deep learning and holography
TLDR
A rapid and stain-free quantitative viral plaque assay using lensfree holographic imaging and deep learning that significantly reduces the incubation time needed for traditional plaque assays while preserving their advantages over other virus quantification methods.

References

SHOWING 1-10 OF 34 REFERENCES
Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning
TLDR
Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.
Rapid and sensitive mycoplasma detection system using image-based deep learning
TLDR
A program comprising three parts (mycoplasma detection, prediction, and cell counting) that allows users to evaluate the sample and verify infected/non-infected cells identified by the program and suggests that the proposed system can realize a low-cost and streamlined manufacturing process for cellular products in cell-based research and clinical applications.
Identification of pathogenic bacteria in complex samples using a smartphone based fluorescence microscope
TLDR
The development of a compact, lightweight and cost-effective smartphone-based fluorescence microscope, capable of detecting signals from fluorescently labeled bacteria, by optimizing a peptide nucleic acid based fluorescence in situ hybridization (FISH) assay is demonstrated.
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
TLDR
This approach has potential for culture-free pathogen identification and antibiotic susceptibility testing, and could be readily extended for diagnostics on blood, urine, and sputum and predict antibiotic treatment from noisy Raman spectra.
An Automated System for Rapid Non-Destructive Enumeration of Growing Microbes
TLDR
A new technology and automated platform that uses digital imaging of cellular autofluorescence to detect and enumerate growing microcolonies many generations before they become visible to the eye is presented.
Quantum dot enabled detection of Escherichia coli using a cell-phone.
TLDR
The results reveal the promising potential of this cell-phone enabled field-portable and cost-effective E. coli detection platform for e.g., screening of water and food samples even in resource limited environments.
Early detection of E. coli and total coliform using an automated, colorimetric and fluorometric fiber optics-based device.
TLDR
It is demonstrated that this cost-effective and automated device, weighing 1.66 kg, can automatically detect the presence of both E. coli and total coliform in drinking water within ∼16 hours, down to a level of one colony-forming unit (CFU) per 100 mL.
Escherichia Coli in Drinking Water
  • J. Appl. Microbiol
  • 2000
...
...